While repeated sampling against a verifier is the standard test-time compute strategy for code generation, measured by pass@K, drawing K independent samples from a single distribution often wastes the compute budget on redundant, near-duplicate reasoning paths. This failure is costly in competitive programming, where many problems admit multiple distinct algorithmic strategies and passing pass@K requires only one correct attempt. We propose **Coordinated Pass@K Policy Optimization (CPPO)**, which trains a joint planner–solver policy for pass@K: a planner emits a tuple of K=4 alternative high-level methods, and a shared solver attempts one solution per method. We train it with a multiplicative planner reward, R_plan = J_𝜓 ⋅ R_out assigning credit only to valid strategy tuples that lead to verifier-confirmed pass@K success. Across APPS, CodeContests, and LiveCodeBench-v6, CPPO improves pass@4 over direct sampling, planning baselines, planner-only SFT, and pass@K-oriented RL under the same K=4 solver-attempt budget, with statistically significant gains on six of nine model–benchmark cells. For instance, it improves Qwen3.5-9B on LiveCodeBench-v6 from 0.588 with PKPO, the strongest baseline, to 0.728, an absolute gain of 0.14 under a hierarchical bootstrap test with p<0.05.